Improved Differentially Private Euclidean Distance Approximation
Summary: Use Kane–Nelson sparse JL sketches (k=Θ(α^-2 log1/β), s=O(α^-1 log1/β)) combined with Laplace/Gaussian mechanisms to produce unbiased, high-utility differentially private sketches for Euclidean distance. Laplace yields pure DP and lower variance than Gaussian when δ < β^{O(1/α)}, and a private FJLT variant trades speed for variance, resolving an open question of Kenthapadi et al. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Nina Mesing Stausholm (BARC; IT University of Copenhagen)
BibTeX Citation
@inproceedings{stausholm_pods21,
address = {New York, NY, USA},
series = {{PODS} '21},
title = {{Improved Differentially Private Euclidean Distance Approximation}},
url = {https://dl.acm.org/doi/10.1145/3452021.3458328},
doi = {10.1145/3452021.3458328},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Stausholm, Nina Mesing},
year = {2021}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,169 | PrivBayes: Private Data Release via Bayesian Networks | 2014 | SIGMOD | 0.00011838753 |
| 1,583 | PriView: Practical Differentially Private Release of Marginal Contingency Tables | 2014 | SIGMOD | 0.00010292522 |
| 2,992 | Pan-private Algorithms Via Statistics on Sketches | 2011 | PODS | 7.8836024e-05 |
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